Aerosol Characterization and Direct Radiative Forcing Assessment over the Ocean. Part I: Methodology and Sensitivity Analysis
|
|
- Lawrence Bridges
- 5 years ago
- Views:
Transcription
1 1799 Aerosol Characterization and Direct Radiative Forcing Assessment over the Ocean. Part I: Methodology and Sensitivity Analysis MARIA JOÃO COSTA Department of Physics, and Évora Geophysics Centre, University of Évora, Évora, Portugal, and National Research Council, Institute of Atmospheric Sciences and Climate (ISAC-CNR), Bologna, Italy ANA MARIA SILVA Department of Physics, and Évora Geophysics Centre, University of Évora, Évora, Portugal VINCENZO LEVIZZANI National Research Council, Institute of Atmospheric Sciences and Climate (ISAC-CNR), Bologna, Italy (Manuscript received 18 July 2003, in final form 19 May 2004) ABSTRACT A method based on the synergistic use of low earth orbit (LEO) and geostationary earth orbit (GEO) satellite data for aerosol-type characterization, as well as aerosol optical thickness (AOT) retrieval and monitoring over the ocean, is presented. These properties are used for the estimation of the direct shortwave aerosol radiative forcing at the top of the atmosphere. The synergy serves the purpose of monitoring aerosol events at the GEO time and space scales while maintaining the accuracy level achieved with LEO instruments. Aerosol optical properties representative of the atmospheric conditions are obtained from the inversion of high-spectral-resolution measurements from the Global Ozone Monitoring Experiment (GOME). The aerosol optical properties are input for radiative transfer calculations for the retrieval of the AOT from GEO visible broadband measurements, avoiding the use of fixed aerosol models available in the literature. The retrieved effective aerosol optical properties represent an essential component for the aerosol radiative forcing assessment. A sensitivity analysis is also presented to quantify the effects that changes on the aerosol model may have on modeled results of spectral reflectance, AOT, and direct shortwave aerosol radiative forcing at the top of the atmosphere. The impact on modeled values of the physical assumptions on surface reflectance and vertical profiles of ozone and water vapor are analyzed. Results show that the aerosol model is the main factor influencing the investigated radiative variables. Results of the application of the method to several significant aerosol events, as well as their validation, are presented in a companion paper. 1. Introduction The growing consciousness of the strong influence of atmospheric aerosol on atmospheric processes (e.g., Houghton et al. 2001), and consequently on climate, prompts local and global studies aimed at quantifying the aerosol load in the atmosphere (aerosol optical thickness: AOT), as well as aerosol optical properties. Aerosol particles play a twofold role in the atmosphere: on one hand they directly scatter and absorb solar radiation, and on the other they enter cloud microphysical processes as cloud condensation nuclei. Certain types of aerosols can even strongly affect the magnitude of precipitation process, as was recently found by Rosenfeld (1999, 2000). Accurate detection, characterization, and Corresponding author address: Vincenzo Levizzani, ISAC-CNR, Via Gobetti 101, I Bologna, Italy. v.levizzani@isac.cnr.it monitoring of aerosol events is therefore essential, since these may affect not only global, but also regional and local climate features (Karyampudi et al. 1999). Geostationary earth orbiting (GEO) satellites ensure the adequate temporal resolution for monitoring the AOT at the global scale (Griggs 1979; Moulin et al. 1997). However, because of their general lack of spectral resolution in their onboard sensors, which prevents a characterization of the aerosol properties, the available aerosol retrieval techniques are constrained to using fixed aerosol models available from the literature. This fact alone can introduce considerable errors into the AOT retrievals and consequently into the top-of-theatmosphere (TOA) direct shortwave aerosol radiative forcing (DSWARF) estimates. Literature models are in fact based on average or standard atmospheric conditions that may not adequately represent the atmospheric state at the time of the satellite overpass. Sensors on board low earth orbit (LEO) satellites do 2004 American Meteorological Society
2 1800 JOURNAL OF APPLIED METEOROLOGY VOLUME 43 not lend themselves to monitoring atmospheric aerosol concentrations because of their poor temporal resolution, which can result in a smoothing of retrieved fields and/or a loss of important features in AOT global maps or regional transport events. Note, however, that the most recent LEO instruments are very valuable tools for aerosol quantitative analysis (King et al. 1999). Their unique multispectral, multiangular, and polarization capabilities allow for improved techniques in retrieving the AOT and aerosol optical properties in general. Tanré et al. (1997) used spectral measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) for a study of the retrieval of the aerosol optical thickness and asymmetry parameter, the relative dominant role of the accumulation or coarse modes, and to a lesser extent the ratio between the modes and the size of the main mode. Veefkind and de Leeuw (1998) developed an algorithm to derive the spectral optical thickness over the ocean from dual-view measurements taken by the Along Track Scanning Radiometer 2 (ATSR-2). Polarization measurements taken by the short-lived Polarization and Directionality of the Earth Reflectances (POLDER) instrument have been used to derive the aerosol optical thickness, the Ångström exponent, and the refractive index (Leroy et al. 1997; Goloub et al. 1999). In spite of its coarse spatial resolution, the Global Ozone Monitoring Experiment (GOME) spectrometer (Burrows et al. 1999) on board the European Remote Sensing Satellite (ERS-2) has been successfully used to retrieve aerosol properties over the ocean. Torricella et al. (1999) presented a method devised not only to detect strong aerosol events, determine the aerosol type, and retrieve high aerosol optical thickness values, but also to correctly detect aerosol load and type over oceanic areas (AOT of the order of 0.1). Ramon et al. (1999) describe an algorithm based on GOME spectral reflectances that is used to derive aerosol optical thickness, aerosol type, and surface type, applicable over ocean and over land surfaces. Bartoloni et al. (2000) developed an operational processing system to derive the aerosol type and optical thickness from GOME spectra. The techniques take advantage of the high spectral resolution of the instrument, trying to avoid gas absorption as much as possible. The novelty of the aerosol property retrieval method proposed here with respect to other satellite-based algorithms already in use is the synergistic use of LEO and GEO sensors. The key target of the method is to overcome the aforementioned limitations of both types of instruments (LEO and GEO) for an effective aerosol optical property monitoring during strong aerosol events over the ocean (Costa et al. 2002). Key features of the method are the improved accuracy of the aerosol characterization with respect to the methods based on GEO measurements and the stretching of the spatial and temporal coverage of the LEO retrievals to the GEO spatiotemporal scale. The ultimate goal is the application of the retrieved aerosol properties in assessing the TOA DSWARF, considering that the uncertainty in quantifying the aerosol radiative forcing is still quite large, especially for strong aerosol events. A more accurate aerosol characterization in terms of its optical properties is therefore crucial to improving the estimates of the induced climate forcing. The inversion of GOME high spectral resolution measurements is instrumental for the retrieval of aerosol spectral optical quantities, such as spectral extinction, spectral single scattering albedo, and phase function. These quantities are then used to derive the AOT from GEO visible (VIS) broadband measurements, avoiding the use of aerosol models from the literature. The aerosol spectral optical quantities characterizing an aerosol transport event, as well as the AOT obtained at the GEO spatiotemporal scale, are finally used to estimate the DSWARF at the TOA. The physical assumptions made in the radiative transfer calculations are tested with a sensitivity analysis. For this purpose, simulation results of the spectral reflectance, AOT, and TOA DSWARF are analyzed using different literature aerosol models, surface characterization, and vertical profiles of water vapor and ozone. The aim is to account for the impact that these assumptions may have on the retrievals. The method is described in the following section, and section 3 presents the analysis of the sensitivity to the aerosol model, surface type, and molecular vertical profile. Conclusions are proposed in section 4. The applications of the method to significant aerosol transport events are contributed in Costa et al. (2004, hereinafter Part II), which discusses the retrieval of aerosol optical quantities, AOT and TOA DSWARF, and their validation with independent ground-based measurements and satellite retrievals. 2. Method The method is based on LEO and GEO satellite measurements and radiative transfer calculations with the aim of merging the fairly good spectral resolution from GOME and the unrivaled spatial and temporal resolution from GEO satellites, specifically Meteosat. The block diagram of the method is shown in Fig. 1 and full details are given by Costa (2004). All radiative transfer calculations are done using the Second Simulation of the Satellite Signal in the Solar Spectrum model (6S; Vermote et al. 1997). Note that Mie theory is applied to spherical aerosol particles. a. Automated GOME spectral measurement selection The retrieval of aerosol quantities from GOME spectral measurements requires a first step consisting of a spectral and geographical pixel selection where aerosol particles are modeled with fewer uncertainties. GOME measures the solar irradiance and the radiance
3 1801 scattered by the earth s atmosphere and surface in the spectral range between and m with a spectral resolution that varies between 0.2 and m and a relative radiometric accuracy of less than 1% (Burrows et al. 1999). Four wavelengths are selected for the present study (at 0.361, 0.421, 0.753, and m) which are characterized by relatively low gas absorption. The geographical areas (pixels) selected to perform the inversions should correspond exclusively to cloudfree areas over the ocean, with overall low reflectance variability that ensures pixel homogeneity. This poses selection problems due to the lack of GOME spectral information in the infrared (IR) spectral region for cloud detection, as well as to the dimension of each GOME pixel ( km 2 ). A way of overcoming this limitation involves the use of GEO data so that the GOME geolocation is matched with the GEO best time coincident classified images (maximum time difference is 15 min, often better). The GEO VIS IR image pairs are classified using the statistical algorithm of Porcù and Levizzani (1992), originally developed for the Meteosat Visible and InfraRed Imager (MVIRI) channels. The technique is used to discriminate between cloud, land, water (background aerosol contamination), and aerosol (strong aerosol contamination) classes. The different spatial resolution of the GEO and GOME sensors is accounted for when overlapping their respective geolocations by determining which GEO image pixels are located inside each GOME pixel. The latter is marked and selected for the inversion only if the contained GEO satellite image pixels are all classified as water or all classified as aerosol contaminated, thus certifying the pixel spatial homogeneity. Figure 2 shows an example of selection in the case of a strong Saharan dust outbreak from 7 (left panel) to 11 June 1997 (right panel). The pixels bounded with the thicker line survived the selection process: one pixel survives in the first scene (classified as water) and four in the second (all classified as aerosol events). The chance of observing cloud-free ground pixels was estimated for the case studies presented in Part II based on the number of pixels selected for the inversion with respect to the number of analyzed pixels, and it was found that in the worst case around 10% of the analyzed pixels were selected for the inversion. The effect of the GOME pixel dimension, that is, the assumption that the aerosol distribution is horizontally homogeneous over the pixel and the use of a single sun satellite geometry, was also assessed through the examination of the GEO radiance values that fall inside each selected GOME pixel and the subsequent error propagation on the retrievals. Results of this analysis are presented in section 3. The first European Meteorological Operational polar satellite (METOP), which is scheduled to be ready for launch by mid-2005, carries the GOME-2 instrument that maintains GOME s spectral characteristics, but greatly increases the spatial resolution to km 2. The present methodology may well be used with GOME-2 data, surely increasing the number of selectable cloud-free homogeneous pixels and improving the confidence in the retrieved mean aerosol model. On the other hand, the use of MODIS or ATSR data in combination with GEO measurements would also increase the number of selectable cloud-free homogeneous pixels, due to the far better spatial resolution of the two sensors with respect to GOME. Their use represents a feasible way of translating the methodology into an operational context and thus contributing to the quantification of aerosol effects on global climate. Note, however, that the confidence in atmospheric correction would be lower because of the broader spectral bands of MODIS and ATSR as compared with GOME high spectral resolution measurements. Therefore, one should use more accurate atmospheric vertical profiles of the gases that have spectral signatures in the bands selected for the aerosol retrievals. b. Aerosol quantities retrieval from GOME Once GOME pixels have been selected, relevant aerosol microphysical parameters can be retrieved by inverting GOME spectral reflectance measurements. In reality, this is a pseudoinversion since it is based on the comparison between the selected measurements and the corresponding simulated spectral reflectance, by varying some of the parameters characterizing the aerosol. Simulations are done considering the ocean surface to be a Lambertian reflector, with a typical spectral reflectance of clear seawater (Viollier 1982) and a tropical type of atmospheric vertical profile (McClatchey et al. 1971). Aerosols are assumed to be characterized by a bimodal lognormal size distribution with a fine and a coarse mode, and a spectral complex refractive index common to both modes. The size distribution and spectral complex refractive index values are taken from the series of climatological aerosol models of Dubovik et al. (2002a), which are based on the analysis of several years of ground-based measurements from the Aerosol Robotic Network (AERONET; Holben et al. 1998) relative to diverse aerosol types and geographic locations. The present GOME spectral reflectance pseudoinversion conducted over wider geographical areas and shorter time periods is then useful in refining these climatological models, which refer to longer time periods and point measurements. The refinement is possible through the variation of some of the size distributions and/or the spectral complex refractive index. The sensitivity of the TOA spectral reflectance, AOT, and TOA DSWARF are investigated with respect to the individual variations of each of the size distribution parameters and the complex refractive index in order to assess which of these parameters most strongly influence the physical quantities under study. Figure 3 presents the TOA spectral reflectance at four wavelengths as a function of the aerosol size distribution parameters (fine- and coarse-mode
4 1802 JOURNAL OF APPLIED METEOROLOGY VOLUME 43 modal radii, fine- and coarse-mode standard deviations, and fine-mode percentage density), the complex refractive index (real and imaginary parts, the latter in two spectral regions: and m), and aerosol optical thickness. This is done for two of the climatological aerosol models described by Dubovik et al. (2002a): Cape Verde (left), representative of desert dust aerosols, and African savannah (right), representative of biomass burning aerosols. The horizontal dashed line in Fig. 3 indicates the spectral reflectance obtained with the climatological model for an AOT value of 0.5. The spectral reflectance variations are obtained by changing the size distribution parameters, the complex refractive index, and the AOT one at a time, and leaving the others fixed by the climatological model, as well as setting the AOT to 0.5. Figure 4 shows the AOT obtained from the GEO radiances, as a function of the aerosol size distribution parameters and complex refractive index for the climatological aerosol models of Fig. 3 (left, Cape Verde; right, African savannah). Also the horizontal dashed line indicates the reference AOT of 0.5, corresponding to the use of the climatological model. As for the previous quantity, the AOT variations are obtained by changing the size distribution parameters and the complex refractive index one at a time, in each case leaving the remaining parameters fixed by the climatological model. Figure 5 illustrates the TOA DSWARF as a function of the aerosol size distribution parameters, complex refractive index, and AOT, for the climatological aerosol models in Fig. 3 (left, Cape Verde; right, African savannah). The horizontal dashed line indicates the TOA DSWARF obtained with the climatological model for an AOT value of 0.5. As before, the TOA DSWARF variations are obtained by changing the size distribution parameters, the complex refractive index, and the AOT one at a time, with the remaining parameters being fixed by the climatological model and with the AOT set to 0.5. The analysis of the graphs in Figs. 3 5 shows that the fine- and coarse-mode standard deviations, as well as the real part of the refractive index, have a small effect on the variation of the spectral reflectance for any of the analyzed wavelengths, on the AOT and on the TOA DSWARF. In most of the cases the sensitivity of the analyzed quantities at the used wavelengths is greater to the fine-mode modal radius than to that of the coarse mode, especially in the cases in which the fraction of smaller particles is greater (biomass burning case). The fine-mode percentage particle density also originates high variations, especially of the spectral reflectances. The imaginary part of the refractive index in the two spectral regions considered is undoubtedly one of the most important parameters to be investigated, together with the AOT (Figs. 3 and 5), which becomes increasingly important in the case of the spectral reflectance at longer wavelengths. This study allowed for the selection of the parameters that mostly influence the spectral reflectance (measured quantity), as well as the AOT and the TOA DSWARF (the key quantities to be derived). Therefore, these parameters (fine-mode modal radius, fine-mode percentage density of particles, imaginary part of the refractive index in two spectral regions, and AOT) are varied in the aerosol characterization used to simulate the spectral reflectance, which is subsequently compared with the GOME spectral reflectance measurements. The spectral regions considered for the imaginary refractive index are and m. The variation of the parameters is done via fixed combinations that depend on the aerosol event type, which is imposed a priori depending on the case under study (geographical location is an important indicator, combined with ancillary data such as trajectory analysis). The variation limits are indicated in Tables 1 and 2. The parameters that originate smaller variations of the analyzed quantities (coarse-mode modal radius, fine- and coarse-mode standard deviations, and real part of the refractive index) are fixed by the respective climatological model, which is established each time according to the case study. The percentage density of particles for each mode is related as follows: pdc 100 pd F, (1) where pd C is the percentage density of particles of the coarse mode and pd F that of the fine particle mode. The distinction between an aerosol event and background aerosol conditions (considered to be described by a maritime aerosol model) is based on the GEO image classification (described in section 2a), which allows for the selection of spatially homogeneous GOME pixels (Fig. 2). If the classification indicates background aerosol contamination within the pixel, the maritime model is chosen; on the contrary, if the classification suggests an aerosol event, the corresponding aerosol model is used according to the case under study. Lookup tables (LUTs) are computed for a number of different geometric conditions and several fixed aerosol models, which are built as explained before, fixing the parameters of the size distribution and complex refractive index and varying the fine-mode modal radius, the FIG. 1. Block diagram of the aerosol retrieval method, which is based on the combination of data from LEO and GEO satellites to derive aerosol properties, AOT, and TOA DSWARF. Data from the GOME spectrometer were used. Gray boxes represent the results and black boxes the validation datasets. In the validation process the aerosol optical properties and AOT are checked against retrievals from sun and sky radiance measurements from the ground-based AERONET. The AOT is also compared with the satellite aerosol official POLDER and MODIS products. The upwelling flux at the TOA is compared with space time-collocated measurements from the Clouds and the Earth s Radiant Energy System (CERES) TOA flux product.
5 1803
6 1804 JOURNAL OF APPLIED METEOROLOGY VOLUME 43 FIG. 2. Selection of GOME pixels by overlapping the GOME geolocation and the Meteosat classified pixels. Four classes are distinguished: water (light gray), land (black), cloud (whitish), and aerosol event over water (dark gray). GOME pixels bounded by thick lines delimit the selections. The examples refer to cases over two limited areas for (left) 7 Jun 1997, GOME orbit 110, and (right) 11 Jun 1997, GOME orbit 122. fine mode percentage density of particles, the imaginary part of the refractive index in two spectral regions ( and m), and the AOT, in the ranges presented in Tables 1 and 2. The measured spectral reflectance (the ratio between the upwelling radiance from the earth s surface and the extraterrestrial solar radiance) corresponding to each selected GOME pixel is compared with the spectral reflectance from the LUTs. First, FIG. 3. Spectral reflectance at four wavelengths as a function of the aerosol size distribution parameters (fine- and coarse-mode modal radius, fine- and coarse-mode standard deviation, and fine-mode percentage density), complex refractive index, and aerosol optical thickness, for two climatological aerosol models described by Dubovik et al. (2002a): (left) Cape Verde, representative of desert dust aerosols, and (right) African savannah, representative of biomass burning aerosols. The horizontal dashed line indicates the spectral reflectance obtained with the climatological model for an aerosol optical thickness of 0.5. The spectral reflectance variations are obtained by changing the size distribution parameters, complex refractive index, and optical thickness one at a time while the other parameters were fixed by the climatological model and while the AOT was set to 0.5.
7 1805 FIG. 4. Aerosol optical thickness obtained from the GEO radiances as a function of the aerosol size distribution parameters and complex refractive index for the same two climatological aerosol models as in Fig. 3. The horizontal dashed line indicates the reference aerosol optical thickness of 0.5, corresponding to the use of the climatological model. The aerosol optical thickness variations are obtained by changing the size distribution parameters and the complex refractive index one at a time, in each case the remaining parameters being fixed by the climatological model. the solar and satellite geometries are identified and the corresponding LUT selected, and second a minimization method is applied to measurements and simulations until the best fit is obtained, leading to the retrieval of the aerosol quantities corresponding to that spectrum (pixel), which are given by the fixed combination of the aerosol parameters (A mod el ) that generated the best fit. The chi-square function used for the minimization is ] 2 [ n G S mod el ( i) ( i; A, a) 2 mod el (A, a ), (2) i 1 ( i) mod el where A are the aerosol models contained in the LUT; a is the aerosol optical thickness; G ( i ) and S ( i ) are the measured and simulated GOME spectral reflectances, respectively; ( i ) is the standard deviation associated with GOME spectral measurements; and n is the number of selected wavelengths i (four GOME channels). The minimization procedure results in Figure 6 shows an example of the fitting result for a selected pixel. Note that the use of the climatological aerosol model for Cape Verde (Dubovik et al. 2002a) does not reproduce measurements as adequately as the derived parameters do. The pseudoinversion is performed over all selected GOME pixels. A subsequent spatiotemporal analysis of the inversion results allows for the retrieval of effective aerosol quantities describing the atmospheric conditions for a certain geographical area and period of time, which replace those from the models in the literature. In all calculations, the aerosol optical quantities (single-scattering albedo, phase function, and extinction coefficient) are automatically obtained assuming spherical aerosol particles; hence, the GOME-derived aerosol microphysics (size distribution and complex refractive index) are used as input for Mie calculations. c. Aerosol optical thickness retrieval from GEO satellite measurements Aerosol optical quantities (single-scattering albedo, phase function, and extinction coefficient) derived from GOME spectral measurements are then combined with data from GEO platforms to retrieve the AOT. Note that the AOT was already retrieved from the minimization process at the GOME pixel scale. By using GEO data the AOT is retrieved at a considerably better spatial resolution, which is suitable for a monitoring strategy over large areas and longer time periods (the MVIRI image repeat cycle is 0.5 h) using the retrieved classes instead of those from climatology. The algorithm can be applied to any GEO satellite
8 1806 JOURNAL OF APPLIED METEOROLOGY VOLUME 43 FIG. 5. TOA DSWARF as a function of the aerosol size distribution parameters, complex refractive index, and aerosol optical thickness for the same two climatological aerosol models as in Fig. 3. The horizontal dashed line indicates the TOA DSWARF obtained with the climatological model for an aerosol optical thickness of 0.5. The TOA DSWARF variations are obtained by changing the size distribution parameters, complex refractive index, and optical thickness one at a time while the other parameters were fixed by the climatological model and while the AOT was set to 0.5. for a possible global coverage strategy. The foreseen use of data from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) would contribute to a further improvement with respect to the present MVIRI, since the sensor has a higher temporal, as well as spatial, resolution: 15-min repeat cycle instead of 30 min, and 3 km at nadir instead of 5 km, respectively. The GEO images are classified using the statistical algorithm of Porcù and Levizzani (1992) to select the cloud-free pixels over the ocean where the AOT will be derived, as well as to distinguish between aerosol event and background aerosol conditions. If the classification indicates background aerosol contamination on the pixel, then the maritime model is taken. On the contrary, if the classification identifies an aerosol event, the corresponding aerosol model is once more used according to the case under study. For each mean aerosol class obtained from the inversion of the GOME spectral reflectance over a certain geographical area and period, an LUT of the GEO VIS broadband radiance is derived that considers all possible geometric conditions (sun and satellite) and seven AOT values (0.0, 0.1, 0.2, 0.5, 1.0, 1.5, and 2.0). Presently, GEO systems are equipped with a broadband VIS spectral channel (MVIRI, m; SEVIRI, and m). The LUT corresponding to each GEO cloud-free pixel is identified taking into account TABLE 1. Size distribution parameters. The lower and upper limits of the parameters allowed to vary are indicated in boldface. The remaining parameter values are assigned according to the test case under study. Aerosol type Mode Modal radius ( m) Biomass burning Desert dust Maritime Fine Coarse Fine Coarse Fine Coarse R F R C R F R C R F R C Std dev of the modal radius ( m) Percentage number density of particles F Pd F : C Pd C Pd F F Pd F : C Pd C Pd F F Pd F : C Pd C Pd F
9 1807 TABLE 2. Complex refractive index parameters. The lower and upper limits of the parameters allowed to vary are indicated in boldface. The real part (n R ) is assigned a fixed value according to the test case under study. Aerosol type Spectral complex refractive index Spectral regions ( m) Aerosol optical thickness Biomass burning Desert dust Maritime n R ( )i n R ( )i n R ( )i n R ( )i n R ( )i n R ( )i the observing geometry and the pixel classification (event or background aerosol type). Subsequently, the AOT corresponding to each of these GEO image pixels is computed by spline interpolation of the GEO radiances stored in the LUT using the GEO radiance measurement value. d. DSWARF assessment from GEO satellite measurements The 6S code computes the downwelling SW flux at SURF the surface, F ; the upwelling spectral reflectance, TOA TOA ; and the spectral radiance, I at the TOA. The TOA extraterrestrial solar spectral irradiance, F, values used in the code are taken from Neckel and Labs (1984). TOA The upwelling SW flux at the TOA ( F ) is obtained here from the spatial integration of the TOA spectral TOA radiance [ I (, )] computed over a grid of satellite (GEO) zenith ( ) and relative azimuth ( ) angles and then integrated from 0.25 to 4.0 m to yield the broadband (SW) flux, applying the following equation: [ ] TOA TOA F I (, ) d d d. (3) All fluxes are calculated for a set of possible solar zenith angle (from 0 to 85 with a step of 5 ) and AOT values (0.0, 0.2, 0.5, 1.0, 1.5, and 2.0), producing LUTs of both the downwelling and upwelling TOA SW fluxes, the net TOA SW flux, and the TOA DSWARF, the last two defined, respectively, as follows: TOA TOA TOA F net F F and (4) TOA TOA TOA F F F, (5) net( aerosol) net( 0) TOA where F is the TOA SW net flux and F TOA net is the TOA direct SW radiative forcing due to the presence of aerosol particles (TOA DSWARF). After determining the solar zenith angle, the AOT retrieved in the previous section from GEO data is compared with the AOT values contained in the LUTs. In this way, the TOA DSWARF and the TOA SW flux in the spectral region between 0.25 and 4.0 m are retrieved. FIG. 6. GOME measurements and simulated spectral reflectance on 11 Jun 1997 using the aerosol model resulting from the fitting process and a desert aerosol model for Cape Verde (Dubovik et al. 2002a): pixel number 1223 was classified as desert type. 3. Sensitivity analysis Some of the physical assumptions made in the radiative transfer calculations are tested with a sensitivity analysis. For this purpose and in order to identify the main uncertainty affecting the radiative quantities entering the present method, the spectral reflectance, the AOT derived from GEO radiances (see computation procedure in section 2c), and the TOA DSWARF are analyzed for a set of possible geometric conditions. The results of these analyses are presented in terms of the differences resulting from adopting different aerosol models from the literature (indicated in Table 3), two different surface optical characterizations [Lambertian versus bidirectional reflectance distribution function (BRDF)], and two different vertical profiles of water vapor and ozone (tropical versus midlatitude winter). In addition, the efficiency of the algorithm proposed to derive the fine-mode modal radius, the fine-mode percentage particle density, the imaginary part of the refractive index in two spectral regions ( and m), and the AOT (at the GOME spatial scale) is tested using synthetic GOME measurements, obtained from calculation with the 6S code. The vertical atmospheric profiles adopted for the analysis are the tropical and the midlatitude winter (Mc- Clatchey et al. 1971), whose ozone and water vapor profiles are shown in Fig. 7. These profiles may represent extreme conditions for the geographical areas where the algorithm is to be applied (GEO satellite cov- TABLE 3. Aerosol models from the literature considered in the sensitivity analysis. Aerosol model Maritime: Lanai Desert dust: Cape Verde Biomass burning: African savannah Reference Dubovik et al. (2002a)
10 1808 JOURNAL OF APPLIED METEOROLOGY VOLUME 43 FIG. 7. Tropical and midlatitude winter vertical profiles of ozone and water vapor (McClatchey et al. 1971) used in the sensitivity analysis. erage); therefore, the resulting differences represent the upper limit for real atmospheric conditions. The results obtained with the assumption of a Lambertian ocean surface, considering a typical spectral reflectance characteristic of clear seawater (Viollier 1982), are compared with those retrieved considering a BRDF for the ocean surface, aiming at quantifying the differences between the two cases. The BRDF ocean model contained in 6S (Morel 1988) is used and takes into account wind speed and direction, ocean salinity, and pigment concentration. Mean values of wind speed of 6ms 1, wind direction of 45, salinity of 35 ppt, and pigment concentration of 0.3 mg m 3 were adopted. Three climatological aerosol models were considered in the sensitivity analysis since they represent fairly different possible atmospheric aerosol scenarios: maritime, desert, and biomass burning defined by Dubovik et al. (2002a) (see Table 3). Figure 8 shows the graphs of the relative differences of the spectral reflectance obtained when changing the assumptions with respect to the vertical atmospheric profiles, surface type, and aerosol model. Results are shown as a function of wavelength for different solar zenith angles, considering an AOT value of 1.0, a satellite zenith angle of 22, and a relative azimuth angle of 150. These angles are chosen because they correspond to a typical GOME geometry. The moderately high AOT value (1.0) is used to establish the maximum errors arising from the different assumptions. The gray vertical dashed lines in each graph in Fig. 8 mark the position of the wavelengths considered in the inversion procedure. Results in Fig. 8a refer to simulations of the spectral reflectance considering a Lambertian ocean surface and the desert aerosol model. As for the vertical characterization, two situations are considered: a tropical and a midlatitude winter vertical profile. These results are subtracted and the respective differences converted in percentages with respect to the value obtained with the tropical profile. The analysis shows that the use of such considerably different vertical atmospheric profiles of water vapor and ozone (see Fig. 7) has a moderate impact on the modeled spectral reflectance, depending on the wavelength. The spectral reflectance relative differences are always 1% (absolute value) for all the wavelengths used (0.361, 0.421, 0.753, and m), as would be reasonably expected since these wavelengths were chosen to avoid gas absorption regions as much as possible. The relative differences of the spectral reflectance presented in Fig. 8b result from considering first the surface as a Lambertian reflector, and second its BRDF. In this case, the atmospheric tropical vertical profile and the desert aerosol model were considered. Relative differences were calculated with respect to values obtained with the Lambertian surface. The results depend on the geometry (solar zenith angle), and in general the resulting differences are lower than 11% (absolute value) in the blue spectral region and 15% 35% (absolute value) in the red spectral region. The spectral reflectance is also analyzed with respect to the considered aerosol model. Figures 8c and 8d illustrate the results obtained when comparing the desert and maritime, and the desert and biomass burning, aerosol models, respectively (see Table 3). In this case, the ocean surface is considered Lambertian and the atmospheric profile is tropical. The spectral reflectance relative differences were calculated with respect to values obtained with the desert aerosol model. The graphs show slightly different behaviors for the two situations. The desert and maritime aerosol models present differences that range between 5% and 30% (absolute value) in the blue spectral region and are generally between 20% and 35% in the red spectral region, except for 0 0, which reaches 60%. When the biomass burning aerosol model is considered, the differences for smaller wavelengths are lower than the previous ones (between 5% and 15%), whereas for the longer wavelengths they are higher, in general between 55% and 70%, and slightly higher for 0 0 as previously discussed, reaching 95%. The analysis of Fig. 8 clearly shows that among the investigated assumptions the aerosol model is the factor that most influences the spectral reflectance. However, the surface characterization may also have an important role, depending on the geometric conditions, as illustrated in Fig. 8b. The AOT absolute differences as a function of the solar zenith angle (obtained from GEO data according to section 2c) are plotted in Fig. 9 for different satellite zenith angles. An AOT reference value of 1.0 is considered, corresponding to the tropical atmospheric profile as an extreme high humidity case, the Lambertian ocean surface, and the desert model in all cases. The AOT differences are obtained by subtracting the values obtained with the different assumptions from the reference AOT value of 1.0. An instrumental random error of 15% of the total GEO signal is considered in the analysis, which stems from the estimation of the Me-
11 1809 FIG. 8. Spectral reflectance relative differences obtained from the different assumptions as a function of wavelength. Simulations are done for an AOT value of 1.0, a satellite zenith angle of 22, and a relative azimuth angle of 150. Differences refer to (a) tropical midlatitude winter vertical profiles, (b) Lambertian surface BRDF consideration, (c) desert maritime aerosol model, and (d) desert biomass burning aerosol model. The vertical dashed lines indicate the wavelengths used for the inversion procedure. teosat errors on calibration [10%; Govaerts (1999)] and spectral response (10% maximum; Y. M. Govaerts 2000, personal communication). Figure 9a shows the AOT differences while considering the midlatitude winter profile instead of the tropical profile used to estimate the reference value. The differences are, in general, within 0.2, except for high solar and satellite zenith angles when the AOT deviates about 0.3 from the AOT reference value of 1.0. The sinusoidal trend is related to the random 15% error considered in the GEO-simulated radiances. The graphs in Fig. 9b show the AOT absolute differences when the surface BRDF is considered, with respect to the reference values derived when considering the ocean surface as a Lambertian reflector. Results exhibit maximum differences ranging from 0.2 (high solar and satellite zenith angles) up to 0.4, which constitute considerable deviations with respect to the AOT reference value of 1.0. The sinusoidal trend is once more introduced by the random 15% error considered. Figure 9c illustrates the results obtained using the maritime aerosol model as compared with the reference values obtained with the desert aerosol model. A moderate dependence on the geometry is found. The highest AOT differences range from about
12 1810 JOURNAL OF APPLIED METEOROLOGY VOLUME 43 FIG. 9. AOT differences as a function of the solar zenith angle for different satellite zenith angles. An AOT value of 1.0 is considered corresponding to a reference LUT calculated using the tropical vertical profile, a Lambertian surface type, and the desert aerosol model. Subsequently, another AOT value is calculated using the reference LUT, from the radiance values obtained considering (a) midlatitude vertical profile, (b) surface BRDF, (c) maritime aerosol model, and (d) biomass burning aerosol model. The AOT differences are obtained from the subtraction of this latter value from the reference value of to 0.6. The plots in Fig. 9d show the AOT absolute differences when the biomass burning aerosol model is assumed, and these differences are compared with the reference value (desert aerosol model). This case is also characterized by high differences in the AOT results with a maximum of 0.8, that is, a deviation of 80% from the reference AOT, and a minimum of 0.1. Analysis of Fig. 9 clearly shows that, among all the investigated possible influences, the aerosol model is the factor that most influences the AOT. Veefkind and de Leeuw (1998) show that an unrealistic aerosol size distribution may introduce substantial errors on the AOT retrievals from 40% to 60%, which is in agreement with the present results. The surface characterization also has a large impact on the AOT that becomes increasingly important for low aerosol loads because the signal received by the satellite is dominated by the surface contribution. The sensitivity analysis of Tanré et al. (1997) has demonstrated that an additional surface contribution results in larger AOT values, and that this effect is more important the smaller the AOT values. Figure 10 shows the graphs of the TOA DSWARF differences as originated with the different assumptions as a function of the AOT for different solar zenith angles. Reference values of the TOA DSWARF are calculated by considering the Lambertian ocean surface, the tropical atmospheric profile, and the desert aerosol model. The vertical dashed gray lines represent the AOT variation limits resulting from the instrumental random error introduced in the GEO-simulated radiances ( 15%) and from the different assumptions (see Fig. 9). The aim is to quantify the uncertainty in the TOA DSWARF from errors introduced in the AOT calculation from GEO data. Figure 10a illustrates the differences with respect to the reference values when calculations are done considering the midlatitude winter profile. Differences in this case are proportional to the AOT values and are mostly independent of the solar zenith angle, reaching about 15 W m 2 for AOT 1.8. The AOT uncertainty of 0.2 introduced by the GEO instrumental error and the use of different atmospheric profiles (see Fig. 9a) generates an uncertainty of about 4Wm 2 in the TOA DSWARF difference. The impact of considering the surface BRDF instead of the Lam-
13 1811 FIG. 10. TOA DSWARF absolute differences derived using the various assumptions considered in the text as a function of the aerosol optical thickness for different solar zenith angles. Differences refer to (a) tropical midlatitude winter vertical profiles, (b) Lambertian surface BRDF consideration, (c) desert maritime aerosol model, and (d) maritime biomass burning aerosol model. bertian surface is displayed in Fig. 10b, where the highest differences (about 12 W m 2 in absolute values) are observed for AOT 1.4 and 0 0 or For a solar zenith angle 0 45, the differences are generally 2.5Wm 2. In Fig. 10b one can see that the propagation of the AOT uncertainty (between 0.2 and 0.4) upon the TOA DSWARF difference is up to about 2Wm 2, which is lower than the one introduced by the vertical profile. These results are inverted with respect to those presented in Figs. 8 and 9, where the surface was a more important assumption than the gaseous profiles. This is connected to the fact that the TOA DSWARF is obtained from flux calculations [Eqs. (3) (5)], which are radiative quantities integrated over a broadband SW spectral region ( m). In this broadband region important gas absorption bands are included, especially water vapor and ozone and consequently the assumption on the gaseous atmospheric constituents becomes more important. The differences arising from the use of the different aerosol models are reported in Figs. 10c and 10d. The first one refers to the use of a maritime aerosol model against the desert model used in the computation of the reference values. The differences are proportional to the AOT with maximum values of around 20 W m 2. The second one corresponds to the comparison between the desert and the biomass burning aerosol models. In this case, the differences are also proportional to the AOT values although with a lower dependence on the solar zenith angle than the one found in the previous case (Fig. 10c). Nevertheless, differences are much higher than in the previous case, with the maximum value is situated around 80Wm 2, whereas in Fig. 10c the maximum value reached down to 20 W m 2. In these cases the GEO AOT uncertainties delimited by the vertical dashed lines ( 0.3 to 0.6 in Fig. 10c and 0.1 to 0.8 in Fig. 10d) generate greater TOA DSWARF difference uncertainties that may reach about 11 W m 2 in the first case (Fig. 10c) and around 30 W m 2 in the second case (Fig. 10d). For all the situations investigated in the present study, the aerosol model is the factor that most influences any of the analyzed radiative quantities: spectral reflectance (Figs. 8c,d), AOT (Figs. 9c,d), and TOA DSWARF (Figs. 10c,d). The retrieval of the effective aerosol quantities is therefore extremely important in order to reduce
14 1812 JOURNAL OF APPLIED METEOROLOGY VOLUME 43 the errors in the retrievals of the aerosol load and its direct radiative forcing, particularly for situations of aerosol events. The adoption of one or the other of the atmospheric vertical profiles has a relatively small impact on the retrievals and this implies that the use of latitudinal- and seasonal-dependent atmospheric profiles is a reasonable approximation to be adopted in the present algorithm. The different surface characterizations originate moderate differences in the results that become more significant for low aerosol loads, as reported by Mishchenko et al. (1999). Since the aim of the present algorithm is the study of aerosol events over the ocean generally distinguished by moderately high aerosol contents and low impact of the surface characterization on the TOA DSWARF, the Lambertian ocean surface may be considered a reasonable approximation. Nevertheless, plans call for LUTs to be built that take into consideration the surface BRDF with the aim of improving the accuracy of the methodology. The methodology is tested using synthetic GOME measurements calculated via the 6S code. The synthetic GOME spectral reflectance values are obtained for 231 cases that consider different geometric conditions, aerosol models, and AOT values. The aerosol model parameters and AOT values used to calculate the synthetic GOME measurements are hereinafter referred to as true values. First, these synthetic data were given as input to the algorithm without changing any assumptions with respect to those considered in the LUTs and the spectral reflectances were measured error free in order to test the algorithm performance. In this case, the values of the fine-mode modal radius, the fine-mode percentage density, the imaginary part of refractive index in the two spectral regions, and the AOT were perfectly retrieved with 100% of the differences peaking at zero (not shown). Successively, the synthetic data were tested for different assumptions of the surface characterization, and of the atmospheric vertical profiles of water vapor and ozone. In addition, values of the fine-mode modal radius, the fine-mode percentage density of particles, the imaginary part of the refractive index in the two spectral regions ( and m), and the AOT not included in the LUTs were also considered. Last, the assumption that the aerosol is homogeneously distributed horizontally over the GOME pixel and the effect of using a single sun satellite geometry were also assessed through the investigation of the best time coincident GEO radiance values (maximum time difference of 15 min) enclosed by the geographical coordinates of the corners of each of the GOME pixels selected for inversion. The mean and standard deviation of the GEO radiance distribution inside each of the analyzed GOME pixels were computed. The relative error was obtained from the ratio between the standard deviation and mean values for each of the pixels. More than 800 pixels selected for the case studies presented in Part II were analyzed and the relative errors averaged, obtaining a mean relative error of 8%. It was assumed that this is the error affecting the GOME spectral reflectance; therefore, a random error of 8% was introduced into the synthetic GOME data. The instrumental radiometric calibration error is considered in all cases by introducing a random noise of 1% to the synthetic data, which is reflected in the GOME relative radiometric accuracy of less than 1% (Burrows et al. 1999). The frequency histograms in Fig. 11 show the differences between the true values and the results from the present methodology considering the following: 1) a different atmospheric profile from that used to build the LUT (midlatitude winter instead of tropical; black bars) and 2) a different surface characterization (BRDF instead of Lambertian; gray bars). The frequency histograms refer to the fine-mode modal radius, the finemode percentage density, and the imaginary part of the refractive index in the two spectral regions, respectively. For the different vertical profiles, the differences are quite low: about 92% of the fine-mode modal radius differences are within m. As for the fine-mode percentage density, more than 95% of the cases are very well retrieved, the differences between the true and derived values being within The differences obtained for the imaginary part of the refractive index are also quite low, that is, about 93% of the values inside the interval for both spectral regions ( and m). When the surface BRDF is considered, the differences are in general higher as would also be expected from the results in the graph in Fig. 8b. The fine-mode modal radius differences present 72% of the cases within m. The fine-mode percentage density is still well retrieved with about 84% of the differences being within As for the imaginary part of the refractive index, the differences are within for 78% of the cases in the first spectral region and 81% of the cases in the second spectral region. The performance of the methodology was also tested for situations in which the aerosol models and AOT values were not included in the LUTs calculation, which may often occur. On the other hand, the effect of the large GOME pixel on the assumption that the aerosol is homogeneously distributed horizontally over the GOME pixel and the effect of using a single sun satellite geometry were also assessed. The frequency histograms in Fig. 12 illustrate the differences between the true values and the results obtained from the present methodology, when 1) the true values are not included in the LUTs (black bars) and 2) a random error of 8% is introduced into the synthetic GOME data to assess the effect of the GOME pixel dimension (gray bars). About 82% of the fine-mode modal radius differences are within m, when the true values are not included in the LUTs, whereas 75% of the values are within m, when the effect of the GOME pixel dimension is considered. The fine-mode percentage density is well retrieved as well with 86% (true values not
Tropospheric aerosol characterization: from GOME towards an ENVISAT perspective
Tropospheric aerosol characterization: from GOME towards an ENVISAT perspective Maria João Costa (1, 2), Marco Cervino (1), Elsa Cattani (1), Francesca Torricella (1), Vincenzo Levizzani (1), Ana Maria
More informationChapter 4 Nadir looking UV measurement. Part-I: Theory and algorithm
Chapter 4 Nadir looking UV measurement. Part-I: Theory and algorithm -Aerosol and tropospheric ozone retrieval method using continuous UV spectra- Atmospheric composition measurements from satellites are
More informationAuthors response to the reviewers comments
Manuscript No.: amtd-3-c1225-2010 Authors response to the reviewers comments Title: Satellite remote sensing of Asian aerosols: A case study of clean, polluted, and Asian dust storm days General comments:
More informationWATER VAPOUR RETRIEVAL FROM GOME DATA INCLUDING CLOUDY SCENES
WATER VAPOUR RETRIEVAL FROM GOME DATA INCLUDING CLOUDY SCENES S. Noël, H. Bovensmann, J. P. Burrows Institute of Environmental Physics, University of Bremen, FB 1, P. O. Box 33 4 4, D 28334 Bremen, Germany
More informationTHE LAND-SAF SURFACE ALBEDO AND DOWNWELLING SHORTWAVE RADIATION FLUX PRODUCTS
THE LAND-SAF SURFACE ALBEDO AND DOWNWELLING SHORTWAVE RADIATION FLUX PRODUCTS Bernhard Geiger, Dulce Lajas, Laurent Franchistéguy, Dominique Carrer, Jean-Louis Roujean, Siham Lanjeri, and Catherine Meurey
More informationSatellite remote sensing of aerosols & clouds: An introduction
Satellite remote sensing of aerosols & clouds: An introduction Jun Wang & Kelly Chance April 27, 2006 junwang@fas.harvard.edu Outline Principals in retrieval of aerosols Principals in retrieval of water
More informationImprovement of the retrieval of aerosol optical properties over oceans using SEVIRI
Improvement of the retrieval of aerosol optical properties over oceans using SEVIRI A. Vermeulen 1, C. Moulin 2, F. Thieuleux 3, I. Chiapello 3, J. Descloitres 1, F. Ducos 3, J-M Nicolas 1, F.-M. Bréon
More informationA new perspective on aerosol direct radiative effects in South Atlantic and Southern Africa
A new perspective on aerosol direct radiative effects in South Atlantic and Southern Africa Ian Chang and Sundar A. Christopher Department of Atmospheric Science University of Alabama in Huntsville, U.S.A.
More informationTHE GLI 380-NM CHANNEL APPLICATION FOR SATELLITE REMOTE SENSING OF TROPOSPHERIC AEROSOL
THE GLI 380-NM CHANNEL APPLICATION FOR SATELLITE REMOTE SENSING OF TROPOSPHERIC AEROSOL Robert Höller, 1 Akiko Higurashi 2 and Teruyuki Nakajima 3 1 JAXA, Earth Observation Research and Application Center
More informationAPPLICATIONS WITH METEOROLOGICAL SATELLITES. W. Paul Menzel. Office of Research and Applications NOAA/NESDIS University of Wisconsin Madison, WI
APPLICATIONS WITH METEOROLOGICAL SATELLITES by W. Paul Menzel Office of Research and Applications NOAA/NESDIS University of Wisconsin Madison, WI July 2004 Unpublished Work Copyright Pending TABLE OF CONTENTS
More informationIn-flight Calibration Techniques Using Natural Targets. CNES Activities on Calibration of Space Sensors
In-flight Calibration Techniques Using Natural Targets CNES Activities on Calibration of Space Sensors Bertrand Fougnie, Patrice Henry (DCT/SI, CNES, Toulouse, France) In-flight Calibration using Natural
More informationVariability in Global Top-of-Atmosphere Shortwave Radiation Between 2000 And 2005
Variability in Global Top-of-Atmosphere Shortwave Radiation Between 2000 And 2005 Norman G. Loeb NASA Langley Research Center Hampton, VA Collaborators: B.A. Wielicki, F.G. Rose, D.R. Doelling February
More informationOptical Theory Basics - 1 Radiative transfer
Optical Theory Basics - 1 Radiative transfer Jose Moreno 3 September 2007, Lecture D1Lb1 OPTICAL THEORY-FUNDAMENTALS (1) Radiation laws: definitions and nomenclature Sources of radiation in natural environment
More informationAtmospheric Lidar The Atmospheric Lidar (ATLID) is a high-spectral resolution lidar and will be the first of its type to be flown in space.
www.esa.int EarthCARE mission instruments ESA s EarthCARE satellite payload comprises four instruments: the Atmospheric Lidar, the Cloud Profiling Radar, the Multi-Spectral Imager and the Broad-Band Radiometer.
More informationInfluence of Clouds and Aerosols on the Earth s Radiation Budget Using Clouds and the Earth s Radiant Energy System (CERES) Measurements
Influence of Clouds and Aerosols on the Earth s Radiation Budget Using Clouds and the Earth s Radiant Energy System (CERES) Measurements Norman G. Loeb Hampton University/NASA Langley Research Center Bruce
More informationTESTS. GRASP sensitivity. Observation Conditions. Retrieval assumptions ISTINA-WP AERO. MODELS. B. Torres, O. Dubovik and D.
TESTS Retrieval assumptions GRASP sensitivity ISTINA-WP3380-2 Observation Conditions AERO. MODELS B. Torres, O. Dubovik and D. Fuertes Introduction Scope of ISTINA-WP3380-2 To establish fundamental limits
More informationCloud property retrievals for climate monitoring:
X-1 ROEBELING ET AL.: SEVIRI & AVHRR CLOUD PROPERTY RETRIEVALS Cloud property retrievals for climate monitoring: implications of differences between SEVIRI on METEOSAT-8 and AVHRR on NOAA-17 R.A. Roebeling,
More informationChanges in Earth s Albedo Measured by satellite
Changes in Earth s Albedo Measured by satellite Bruce A. Wielicki, Takmeng Wong, Norman Loeb, Patrick Minnis, Kory Priestley, Robert Kandel Presented by Yunsoo Choi Earth s albedo Earth s albedo The climate
More informationACTRIS TNA Activity Report
ACTRIS TNA Activity Report Characterization of Aerosol mixtures of Dust And MArine origin by synergy of lidar, sunphotometer and surface/airborne in situ, ADAMA Natalia Kouremeti Introduction and motivation
More informationLecture 3: Atmospheric Radiative Transfer and Climate
Lecture 3: Atmospheric Radiative Transfer and Climate Solar and infrared radiation selective absorption and emission Selective absorption and emission Cloud and radiation Radiative-convective equilibrium
More informationGeostationary Earth Radiation Budget Project: Status and Results
Geostationary Earth Radiation Budget Project: Status and Results J. A. Hanafin, J. E. Harries, J. E. Russell, J. M. Futyan, H. Brindley, S. Kellock, S. Dewitte1, P. M. Allan2 Space and Atmospheric Physics,
More informationHistory of Earth Radiation Budget Measurements With results from a recent assessment
History of Earth Radiation Budget Measurements With results from a recent assessment Ehrhard Raschke and Stefan Kinne Institute of Meteorology, University Hamburg MPI Meteorology, Hamburg, Germany Centenary
More informationAssessing the Radiative Impact of Clouds of Low Optical Depth
Assessing the Radiative Impact of Clouds of Low Optical Depth W. O'Hirok and P. Ricchiazzi Institute for Computational Earth System Science University of California Santa Barbara, California C. Gautier
More informationLong-Term Time Series of Water Vapour Total Columns from GOME, SCIAMACHY and GOME-2
Graphics: ESA Graphics: ESA Graphics: ESA Long-Term Time Series of Water Vapour Total Columns from GOME, SCIAMACHY and GOME-2 S. Noël, S. Mieruch, H. Bovensmann, J. P. Burrows Institute of Environmental
More informationSpectral surface albedo derived from GOME-2/Metop measurements
Spectral surface albedo derived from GOME-2/Metop measurements Bringfried Pflug* a, Diego Loyola b a DLR, Remote Sensing Technology Institute, Rutherfordstr. 2, 12489 Berlin, Germany; b DLR, Remote Sensing
More informationInitial Assessment of the Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR)-Based Aerosol Retrieval: Sensitivity Study
Atmosphere 212, 3, 495-521; doi:1.339/atmos34495 Article OPEN ACCESS atmosphere ISSN 273-4433 www.mdpi.com/journal/atmosphere Initial Assessment of the Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric
More informationSolar Insolation and Earth Radiation Budget Measurements
Week 13: November 19-23 Solar Insolation and Earth Radiation Budget Measurements Topics: 1. Daily solar insolation calculations 2. Orbital variations effect on insolation 3. Total solar irradiance measurements
More informationSatellite observation of atmospheric dust
Satellite observation of atmospheric dust Taichu Y. Tanaka Meteorological Research Institute, Japan Meteorological Agency 11 April 2017, SDS WAS: Dust observation and modeling @WMO, Geneva Dust observations
More informationRadiation in the atmosphere
Radiation in the atmosphere Flux and intensity Blackbody radiation in a nutshell Solar constant Interaction of radiation with matter Absorption of solar radiation Scattering Radiative transfer Irradiance
More informationSpectrum of Radiation. Importance of Radiation Transfer. Radiation Intensity and Wavelength. Lecture 3: Atmospheric Radiative Transfer and Climate
Lecture 3: Atmospheric Radiative Transfer and Climate Radiation Intensity and Wavelength frequency Planck s constant Solar and infrared radiation selective absorption and emission Selective absorption
More informationCross-calibration of Geostationary Satellite Visible-channel Imagers Using the Moon as a Common Reference
Cross-calibration of Geostationary Satellite Visible-channel Imagers Using the Moon as a Common Reference Thomas C. Stone U.S. Geological Survey, Flagstaff AZ, USA 27 30 August, 2012 Motivation The archives
More informationMSG system over view
MSG system over view 1 Introduction METEOSAT SECOND GENERATION Overview 2 MSG Missions and Services 3 The SEVIRI Instrument 4 The MSG Ground Segment 5 SAF Network 6 Conclusions METEOSAT SECOND GENERATION
More informationGlobal observations and spectral characteristics of desert dust and biomass burning aerosols
Global observations and spectral characteristics of desert dust and biomass burning aerosols M. de Graaf & P. Stammes Royal Netherlands Meteorological Institute (KNMI) P.O. Box 201, 3730 AE De Bilt, The
More informationA unified, global aerosol dataset from MERIS, (A)ATSR and SEVIRI
A unified, global aerosol dataset from MERIS, and SEVIRI Gareth Thomas gthomas@atm.ox.ac.uk Introduction GlobAEROSOL is part of the ESA Data User Element programme. It aims to provide a global aerosol
More informationAtmospheric Correction Using Hyperion
Atmospheric Correction Using Hyperion Progress and Issues Investigation: Correlative Analysis of EO-1, Landsat, and Terra Data of the DOE ARM CART Sites: An Investigation of Instrument Performance and
More informationRegional evaluation of an advanced very high resolution radiometer (AVHRR) two-channel aerosol retrieval algorithm
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109,, doi:10.1029/2003jd003817, 2004 Regional evaluation of an advanced very high resolution radiometer (AVHRR) two-channel aerosol retrieval algorithm Tom X.-P. Zhao,
More informationSATELLITE RETRIEVAL OF AEROSOL PROPERTIES OVER BRIGHT REFLECTING DESERT REGIONS
SATELLITE RETRIEVAL OF AEROSOL PROPERTIES OVER BRIGHT REFLECTING DESERT REGIONS Tilman Dinter 1, W. von Hoyningen-Huene 1, A. Kokhanovsky 1, J.P. Burrows 1, and Mohammed Diouri 2 1 Institute of Environmental
More informationPolar Multi-Sensor Aerosol Product: User Requirements
Polar Multi-Sensor Aerosol Product: User Requirements Doc.No. Issue : : EUM/TSS/REQ/13/688040 v2 EUMETSAT EUMETSAT Allee 1, D-64295 Darmstadt, Germany Tel: +49 6151 807-7 Fax: +49 6151 807 555 Telex: 419
More informationStatus of Libya-4 Activities - RAL
Status of Libya-4 Activities - RAL Dr David L Smith Preparation for reprocessing AATSR Long term drift correction LUT version 2.09 implemented in reprocessing V3.00 available based on revised BRF modelling
More informationLecture 4: Radiation Transfer
Lecture 4: Radiation Transfer Spectrum of radiation Stefan-Boltzmann law Selective absorption and emission Reflection and scattering Remote sensing Importance of Radiation Transfer Virtually all the exchange
More informationEvaluation of Satellite and Reanalysis Products of Downward Surface Solar Radiation over East Asia
International Workshop on Land Use/Cover Changes and Air Pollution in Asia August 4-7th, 2015, Bogor, Indonesia Evaluation of Satellite and Reanalysis Products of Downward Surface Solar Radiation over
More informationLecture 3. Background materials. Planetary radiative equilibrium TOA outgoing radiation = TOA incoming radiation Figure 3.1
Lecture 3. Changes in planetary albedo. Is there a clear signal caused by aerosols and clouds? Outline: 1. Background materials. 2. Papers for class discussion: Palle et al., Changes in Earth s reflectance
More informationVerification of Sciamachy s Reflectance over the Sahara J.R. Acarreta and P. Stammes
Verification of Sciamachy s Reflectance over the Sahara J.R. Acarreta and P. Stammes Royal Netherlands Meteorological Institute P.O. Box 201, 3730 AE de Bilt, The Netherlands Email Address: acarreta@knmi.nl,
More informationMERIS, A-MODIS, SeaWiFS, AATSR and PARASOL over the Salar de Uyuni March 2006 MAVT 2006 Marc Bouvet, ESA/ESTEC
MERIS, A-MODIS, SeaWiFS, AATSR and PARASOL over the Salar de Uyuni Plan of the presentation 1. Introduction : from absolute vicarious calibration to radiometric intercomparison 2. Intercomparison at TOA
More informationHyperspectral Atmospheric Correction
Hyperspectral Atmospheric Correction Bo-Cai Gao June 2015 Remote Sensing Division Naval Research Laboratory, Washington, DC USA BACKGROUND The concept of imaging spectroscopy, or hyperspectral imaging,
More informationComparison of AERONET inverted size distributions to measured distributions from the Aerodyne Aerosol Mass Spectrometer
Comparison of inverted size distributions to measured distributions from the Aerodyne Aerosol Mass Spectrometer Peter DeCarlo Remote Sensing Project April 28, 23 Introduction The comparison of direct in-situ
More informationApplication of a Land Surface Temperature Validation Protocol to AATSR data. Dar ren Ghent1, Fr ank Göttsche2, Folke Olesen2 & John Remedios1
Application of a Land Surface Temperature Validation Protocol to AATSR data Dar ren Ghent1, Fr ank Göttsche, Folke Olesen & John Remedios1 1 E a r t h O b s e r v a t i o n S c i e n c e, D e p a r t m
More informationMenzel/Matarrese/Puca/Cimini/De Pasquale/Antonelli Lab 2 Ocean Properties inferred from MODIS data June 2006
Menzel/Matarrese/Puca/Cimini/De Pasquale/Antonelli Lab 2 Ocean Properties inferred from MODIS data June 2006 Table: MODIS Channel Number, Wavelength (µm), and Primary Application Reflective Bands Emissive
More informationSimulation of UV-VIS observations
Simulation of UV-VIS observations Hitoshi Irie (JAMSTEC) Here we perform radiative transfer calculations for the UV-VIS region. In addition to radiance spectra at a geostationary (GEO) orbit, air mass
More informationOptical properties and radiative forcing of the Eyjafjallajökull volcanic ash layer observed over Lille, France, in 2010
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117,, doi:10.1029/2011jd016815, 2012 Optical properties and radiative forcing of the Eyjafjallajökull volcanic ash layer observed over Lille, France, in 2010 Y. Derimian,
More informationUKCA_RADAER Aerosol-radiation interactions
UKCA_RADAER Aerosol-radiation interactions Nicolas Bellouin UKCA Training Workshop, Cambridge, 8 January 2015 University of Reading 2014 n.bellouin@reading.ac.uk Lecture summary Why care about aerosol-radiation
More informationRETRIEVAL OF AEROSOL PROPERTIES FROM SEVIRI USING VISIBLE AND INFRA-RED CHANNELS.
RETRIEVAL OF AEROSOL PROPERTIES FROM SEVIRI USING VISIBLE AND INFRA-RED CHANNELS. Elisa Carboni (1), Gareth Thomas (1), Roy Grainger (1), Caroline Poulsen (2), Richard Siddans (2), Daniel Peters (1), Elies
More informationAerosol measurements from Space. Gerrit de Leeuw FMI & Uni of Helsinki, Finland & TNO, Utrecht, Netherlands
Aerosol measurements from Space Gerrit de Leeuw FMI & Uni of Helsinki, Finland & TNO, Utrecht, Netherlands ACCENT AT-2 Follow-up meeting Mainz, 22 June 2009 ACCENT AT-2 Outcomes The Remote Sensing of Tropospheric
More informationInterannual variability of top-ofatmosphere. CERES instruments
Interannual variability of top-ofatmosphere albedo observed by CERES instruments Seiji Kato NASA Langley Research Center Hampton, VA SORCE Science team meeting, Sedona, Arizona, Sep. 13-16, 2011 TOA irradiance
More informationFIRST VALIDATION OF MERIS AEROSOL PRODUCT OVER LAND
ABSTRACT FIRST VALIDATION OF MERIS AEROSOL PRODUCT OVER LAND Didier Ramon (1), Richard Santer (2), Jerôme Vidot (2) 1. HYGEOS, 191 rue N. Appert, 59650 Villeneuve d Ascq, France, dr@hygeos.com 2. Université
More informationSaharan Dust Longwave Radiative Forcing using GERB and SEVIRI
Imperial College London Saharan Dust Longwave Radiative Forcing using GERB and SEVIRI Vincent Gimbert 1, H.E. Brindley 1, Nicolas Clerbaux 2, J.E. Harries 1 1. Blackett Laboratory, Imperial College, London
More informationGSICS UV Sub-Group Activities
GSICS UV Sub-Group Activities Rosemary Munro with contributions from NOAA, NASA and GRWG UV Subgroup Participants, in particular L. Flynn 1 CEOS Atmospheric Composition Virtual Constellation Meeting (AC-VC)
More informationCalibration of Ocean Colour Sensors
Dr. A. Neumann German Aerospace Centre DLR Remote Sensing Technology Institute Marine Remote Sensing What is Calibration, why do we need it? Sensor Components Definition of Terms Calibration Standards
More informationCLOUD CLASSIFICATION AND CLOUD PROPERTY RETRIEVAL FROM MODIS AND AIRS
6.4 CLOUD CLASSIFICATION AND CLOUD PROPERTY RETRIEVAL FROM MODIS AND AIRS Jun Li *, W. Paul Menzel @, Timothy, J. Schmit @, Zhenglong Li *, and James Gurka # *Cooperative Institute for Meteorological Satellite
More informationAn Overview of the Radiation Budget in the Lower Atmosphere
An Overview of the Radiation Budget in the Lower Atmosphere atmospheric extinction irradiance at surface P. Pilewskie 300 University of Colorado Laboratory for Atmospheric and Space Physics Department
More informationSimulated Radiances for OMI
Simulated Radiances for OMI document: KNMI-OMI-2000-004 version: 1.0 date: 11 February 2000 author: J.P. Veefkind approved: G.H.J. van den Oord checked: J. de Haan Index 0. Abstract 1. Introduction 2.
More informationWhat are Aerosols? Suspension of very small solid particles or liquid droplets Radii typically in the range of 10nm to
What are Aerosols? Suspension of very small solid particles or liquid droplets Radii typically in the range of 10nm to 10µm Concentrations decrease exponentially with height N(z) = N(0)exp(-z/H) Long-lived
More informationEvaluation of aerosol optical depth and aerosol models from VIIRS retrieval algorithms over North China Plain
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Papers in the Earth and Atmospheric Sciences Earth and Atmospheric Sciences, Department of 2017 Evaluation of aerosol optical
More informationSATELLITE OBSERVATIONS OF CLOUD RADIATIVE FORCING FOR THE AFRICAN TROPICAL CONVECTIVE REGION
SATELLITE OBSERVATIONS OF CLOUD RADIATIVE FORCING FOR THE AFRICAN TROPICAL CONVECTIVE REGION J. M. Futyan, J. E. Russell and J. E. Harries Space and Atmospheric Physics Group, Blackett Laboratory, Imperial
More informationReport Benefits and Challenges of Geostationary Ocean Colour Remote Sensing - Science and Applications. Antonio Mannino & Maria Tzortziou
Report Benefits and Challenges of Geostationary Ocean Colour Remote Sensing - Science and Applications Antonio Mannino & Maria Tzortziou Time & Space Scales of OC Relevant Missions GOCI I & II Geo from
More informationStudy of the Influence of Thin Cirrus Clouds on Satellite Radiances Using Raman Lidar and GOES Data
Study of the Influence of Thin Cirrus Clouds on Satellite Radiances Using Raman Lidar and GOES Data D. N. Whiteman, D. O C. Starr, and G. Schwemmer National Aeronautics and Space Administration Goddard
More informationRadCalNet Quick Start Guide. RadCalNet Quick Start Guide
RadCalNet Quick Start Guide RadCalNet Quick Start Guide 1. Scope of the document... 2 2. How to access the RadCalNet data?... 2 3. RadCalNet: which data for which purpose?... 2 4. How to use the data?...
More informationComparison of Aircraft Observed with Calculated Downwelling Solar Fluxes during ARESE Abstract
Comparison of Aircraft Observed with Calculated Downwelling Solar Fluxes during ARESE Abstract The objectives of the Atmospheric Radiation Measurement (ARM) Enhanced Shortwave Experiment (ARESE) are to
More informationAerosol direct radiative effect at the top of the atmosphere over cloud free ocean derived from four years of MODIS data
www.atmos-chem-phys.org/acp/6/237/ SRef-ID: 1680-7324/acp/2006-6-237 European Geosciences Union Atmospheric Chemistry and Physics Aerosol direct radiative effect at the top of the atmosphere over cloud
More informationAerosol Optical Depth Variation over European Region during the Last Fourteen Years
Aerosol Optical Depth Variation over European Region during the Last Fourteen Years Shefali Singh M.Tech. Student in Computer Science and Engineering at Meerut Institute of Engineering and Technology,
More informationC M E M S O c e a n C o l o u r S a t e l l i t e P r o d u c t s
Implemented by C M E M S O c e a n C o l o u r S a t e l l i t e P r o d u c t s This slideshow gives an overview of the CMEMS Ocean Colour Satellite Products Marine LEVEL1 For Beginners- Slides have been
More informationClouds, Haze, and Climate Change
Clouds, Haze, and Climate Change Jim Coakley College of Oceanic and Atmospheric Sciences Earth s Energy Budget and Global Temperature Incident Sunlight 340 Wm -2 Reflected Sunlight 100 Wm -2 Emitted Terrestrial
More informationRain rate retrieval using the 183-WSL algorithm
Rain rate retrieval using the 183-WSL algorithm S. Laviola, and V. Levizzani Institute of Atmospheric Sciences and Climate, National Research Council Bologna, Italy (s.laviola@isac.cnr.it) ABSTRACT High
More informationThe observation of the Earth Radiation Budget a set of challenges
The observation of the Earth Radiation Budget a set of challenges Dominique Crommelynck, Steven Dewitte, Luis Gonzalez,Nicolas Clerbaux, Alessandro Ipe, Cedric Bertrand. (Royal Meteorological Institute
More informationTopics: Visible & Infrared Measurement Principal Radiation and the Planck Function Infrared Radiative Transfer Equation
Review of Remote Sensing Fundamentals Allen Huang Cooperative Institute for Meteorological Satellite Studies Space Science & Engineering Center University of Wisconsin-Madison, USA Topics: Visible & Infrared
More informationHICO Calibration and Atmospheric Correction
HICO Calibration and Atmospheric Correction Curtiss O. Davis College of Earth Ocean and Atmospheric Sciences Oregon State University, Corvallis, OR, USA 97331 cdavis@coas.oregonstate.edu Oregon State Introduction
More informationGround-based Validation of spaceborne lidar measurements
Ground-based Validation of spaceborne lidar measurements Ground-based Validation of spaceborne lidar measurements to make something officially acceptable or approved, to prove that something is correct
More informationSCIAMACHY REFLECTANCE AND POLARISATION VALIDATION: SCIAMACHY VERSUS POLDER
SCIAMACHY REFLECTANCE AND POLARISATION VALIDATION: SCIAMACHY VERSUS POLDER L. G. Tilstra (1), P. Stammes (1) (1) Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, 3730 AE de Bilt, The Netherlands
More informationCURRENT STATUS OF SCIAMACHY POLARISATION MEASUREMENTS. J.M. Krijger 1 and L.G. Tilstra 2
% % CURRENT STATUS OF SCIAMACHY POLARISATION MEASUREMENTS JM Krijger 1 and LG Tilstra 2 1 SRON (National Institute for Space Research), Sorbonnelaan 2, 3584 CA Utrecht, The Netherlands, krijger@sronnl
More information2nd Annual CICS-MD Science Meeting November 6-7, 2013 Earth System Science Interdisciplinary Center University of Maryland, College Park, MD
Development of Algorithms for Shortwave Radiation Budget from GOES-R R. T. Pinker, M. M. Wonsick GOES-R Algorithm Working Group Radiation Budget Application Team John A. Augustine (NOAA); Hye-Yun Kim (IMSG);
More informationInversion of Sun & Sky Radiance to Derive Aerosol Properties from AERONET
Inversion of Sun & Sky Radiance to Derive Aerosol Properties from AERONET Oleg Dubovik (GEST/UMBC, NASA/GSFC) Contributors: Brent Holben,, Alexander Smirnov, Tom Eck, Ilya Slutsker, Tatyana Lapyonok, AERONET
More informationIn-Orbit Vicarious Calibration for Ocean Color and Aerosol Products
In-Orbit Vicarious Calibration for Ocean Color and Aerosol Products Menghua Wang NOAA National Environmental Satellite, Data, and Information Service Office of Research and Applications E/RA3, Room 12,
More informationRadiation Quantities in the ECMWF model and MARS
Radiation Quantities in the ECMWF model and MARS Contact: Robin Hogan (r.j.hogan@ecmwf.int) This document is correct until at least model cycle 40R3 (October 2014) Abstract Radiation quantities are frequently
More informationThe Spectral Radiative Effects of Inhomogeneous Clouds and Aerosols
The Spectral Radiative Effects of Inhomogeneous Clouds and Aerosols S. Schmidt, B. Kindel, & P. Pilewskie Laboratory for Atmospheric and Space Physics University of Colorado SORCE Science Meeting, 13-16
More informationand Atmospheric Science, University of Miami, 4600 Rickenbacker Causeway, Miami, FL 33149, USA.
Observed changes in top-of-the-atmosphere radiation and upper-ocean heating consistent within uncertainty. Steady accumulation of heat by Earth since 2000 according to satellite and ocean data Norman G.
More informationRETRIEVAL OF AEROSOL OPTICAL DEPTH OVER URBAN AREAS USING TERRA/MODIS DATA
RETRIEVAL OF AEROSOL OPTICAL DEPTH OVER URBAN AREAS USING TERRA/MODIS DATA X. Q. Zhang a, *, L. P. Yang b, Y. Yamaguchi a a Dept. of Earth and Environmental Sciences, Graduate School of Environmental Studies,
More informationIdentifying the regional thermal-ir radiative signature of mineral dust with MODIS
GEOPHYSICAL RESEARCH LETTERS, VOL. 32, L16803, doi:10.1029/2005gl023092, 2005 Identifying the regional thermal-ir radiative signature of mineral dust with MODIS Anton Darmenov and Irina N. Sokolik School
More informationAn Algorithm for the Retrieval of Aerosol Optical Depth from Geostationary Satellite Data in Thailand
TUTA/IOE/PCU SAHR Journal of the Institute of Engineering, Vol. 8, No. 3, pp. 32 41 TUTA/IOE/PCU All rights reserved. Printed in Nepal Fax: 977-1-5525830 An Algorithm for the Retrieval of Aerosol Optical
More informationVIIRS SDR Cal/Val: S-NPP Update and JPSS-1 Preparations
VIIRS SDR Cal/Val: S-NPP Update and JPSS-1 Preparations VIIRS SDR Cal/Val Posters: Xi Shao Zhuo Wang Slawomir Blonski ESSIC/CICS, University of Maryland, College Park NOAA/NESDIS/STAR Affiliate Spectral
More informationPreliminary testing of new approaches to retrieve aerosol properties from joint photometer-lidar inversion
ESA/IDEAS Project- WP 3440-2 Preliminary testing of new approaches to retrieve aerosol properties from joint photometer-lidar inversion Q. Hu, P. Goloub, O. Dubovik, A. Lopatin, T. Povdin, T. Lopyonok,
More informationGMES: calibration of remote sensing datasets
GMES: calibration of remote sensing datasets Jeremy Morley Dept. Geomatic Engineering jmorley@ge.ucl.ac.uk December 2006 Outline Role of calibration & validation in remote sensing Types of calibration
More informationBulk aerosol optical properties over the western North Pacific estimated by MODIS and CERES measurements : Coastal sea versus Open sea
Bulk aerosol optical properties over the western North Pacific estimated by MODIS and CERES measurements : Coastal sea versus Open sea Hye-Ryun Oh 1, Yong-Sang Choi 1, Chang-Hoi Ho 1, Rokjin J. Park 1,
More informationThe EarthCARE mission: An active view on aerosols, clouds and radiation
The EarthCARE mission: An active view on aerosols, clouds and radiation T. Wehr, P. Ingmann, T. Fehr Heraklion, Crete, Greece 08/06/2015 EarthCARE is ESA s sixths Earth Explorer Mission and will be implemented
More informationAerosol Retrieved from MODIS: Algorithm, Products, Validation and the Future
Aerosol Retrieved from MODIS: Algorithm, Products, Validation and the Future Presented by: Rob Levy Re-presenting NASA-GSFC s MODIS aerosol team: Y. Kaufman, L. Remer, A. Chu,, C. Ichoku,, R. Kleidman,,
More informationAerosol impact and correction on temperature profile retrieval from MODIS
GEOPHYSICAL RESEARCH LETTERS, VOL. 35, L13818, doi:10.1029/2008gl034419, 2008 Aerosol impact and correction on temperature profile retrieval from MODIS Jie Zhang 1,2 and Qiang Zhang 1,2 Received 24 April
More informationPreface to the Second Edition. Preface to the First Edition
Contents Preface to the Second Edition Preface to the First Edition iii v 1 Introduction 1 1.1 Relevance for Climate and Weather........... 1 1.1.1 Solar Radiation.................. 2 1.1.2 Thermal Infrared
More informationHARP Assessment of Uncertainty
HARP Assessment of Uncertainty The HIAPER Airborne Radiation Package (HARP) was designed to produce accurate measurements of actinic flux and irradiance. The Atmospheric Radiation Group (ARG) at the University
More informationTOTAL COLUMN OZONE AND SOLAR UV-B ERYTHEMAL IRRADIANCE OVER KISHINEV, MOLDOVA
Global NEST Journal, Vol 8, No 3, pp 204-209, 2006 Copyright 2006 Global NEST Printed in Greece. All rights reserved TOTAL COLUMN OZONE AND SOLAR UV-B ERYTHEMAL IRRADIANCE OVER KISHINEV, MOLDOVA A.A. ACULININ
More informationLand Surface Temperature in the EUMETSAT LSA SAF: Current Service and Perspectives. Isabel Trigo
Land Surface Temperature in the EUMETSAT LSA SAF: Current Service and Perspectives Isabel Trigo Outline EUMETSAT Land-SAF: Land Surface Temperature Geostationary Service SEVIRI Polar-Orbiter AVHRR/Metop
More informationProjects in the Remote Sensing of Aerosols with focus on Air Quality
Projects in the Remote Sensing of Aerosols with focus on Air Quality Faculty Leads Barry Gross (Satellite Remote Sensing), Fred Moshary (Lidar) Direct Supervision Post-Doc Yonghua Wu (Lidar) PhD Student
More information